Báo cáo khoa học: "Creating a CCGbank and a wide-coverage CCG lexicon for German" pdf

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 505–512,
Sydney, July 2006.
c
2006 Association for Computational Linguistics
Creating a CCGbank and a wide-coverage CCG lexicon for German
Julia Hockenmaier
Institute for Research in Cognitive Science
University of Pennsylvania
Philadelphia, PA 19104, USA

Abstract
We present an algorithm which creates a
German CCGbank by translating the syn-
tax g raphs in the German Tiger corpus into
CCG derivation trees. The resulting cor-
pus contains 46,628 derivations, covering
95% of all complete sentences in Tiger.
Lexicons ex tracted from this corpus con-
tain correct lexical entries for 94% of all
known tokens in unseen text.
1 Introduction
A number of wide-coverage TAG , CCG, LFG and
HPSG grammars (Xia, 1999; Chen et al., 2005;
Hockenmaier and Steedman, 2002a; O’Donovan
et al., 2005; Miyao et al., 2004) have been ex-
tracted from the Penn Treebank (Marcus et al.,
1993), and have enabled the creation of wide-
cov erage parsers for English which recover local
and non-local dependencies that approximate the
underlying predicate-argument structure (Hocken-
maier and Steedman, 2002b; Clark and Curran,

2004; Miyao and Tsujii, 2005; Shen and Joshi,
2005). However, many corpora (B¨ohomv´aetal.,
2003; Skut et al., 1997; Brants et al., 2002) use
dependency graphs or other representations, and
the extraction algorithms that have been developed
for Penn Treebank style corpora may not be im-
mediately applicable to this representation. As a
consequence, research on statistical parsing with
“deep” grammars has largely been confinedtoEn-
glish. Free-word order languages typically pose
greater challenges for syntactic theories (Rambow,
1994), and the richer inflectional morphology of
these languages creates additional problems both
for the coverage of lexicalized formalisms such
as CCG or TAG, and for the usefulness of de-
pendency counts extracted from the training data.
On the other hand, formalisms such as CCG and
TAG a re particularly suited to capture the cross-
ing dependencies that arise in languages such as
Dutch or German, and by choosing an appropriate
linguistic representation, some of these problems
may be mitigated.
Here, we present an algorithm which translates
the German Tiger corpus (Brants et al., 2002) into
CCG derivations. Similar algorithms ha ve been
developed by Hockenmaier and Steedman (2002a)
to create CCGbank, a corpus of CCG derivations
(Hockenmaier and Steedman, 2005) from the Penn
T reebank, by C¸akıcı (2005) to extract a CCG lex-
icon from a Turkish dependency corpus, and by

Moortgat and Moot (2002) to induce a type-logical
grammar for Dutch.
The annotation scheme used in Tiger is an ex-
tension o f that used in the earlier, and smaller,
German Negra corpus (Skut et al., 1997). Tiger
is better suited for the extraction of subcatego-
rization information (and thus the translation into
“deep” grammars of any kind), since it distin-
guishes between PP complements and modifiers,
and includes “secondary” edges to indicate shared
arguments in coordinate constructions. Tiger also
includes morphology and lemma information.
Negra is also provided with a “Penn Treebank”-
style representation, which uses flat phrase s truc-
ture trees instead of the crossing dependency
structures in the original corpus. This version
has been used by Cahill et al. ( 2005) to extract a
German LFG. However, Dubey and Keller (2003)
have demonstrated that le xicalization does not
help a Collins-style parser that is trained on this
corpus, and Levy and Manning (2004) have shown
that its context-free representation is a poor ap-
proximation to the underlying dependency struc-
ture. The resource presented here will enable
future research to address the question whether
“deep” grammars such as CCG, which capture the
underlying dependencies directly, are better suited
to parsing German than linguistically inadequate
context-free approximations.
505

1. Standard main clause
Peter gibt Maria das Buch
2. Main clause with fronted adjunct 3. Main clause with fronted complement
dann gibt Peter Maria das Buch Maria gibt Peter das Buch
Figure 1: CCG uses topicalization (1.), a type-changing rule (2.), and type-raising (3.) to capture the
different variants of German main clause order with the same lexical category for the verb.
2 German syntax and morphology
Morphology German verbs are inflected for
person, number, tense and mood. German nouns
and adjecti ves are inflected for number, case and
gender, and noun compounding is very productive.
Word order German has three different word
orders that depend on the clause type. Main
clauses (1) are verb-second. Imperatives and ques-
tions are verb-initial (2). If a modifier or one of
the objects is moved to the front, the word order
becomes verb-initial (2). Subordinate and relativ e
clauses are verb-final (3):
(1) a. Peter gibt Maria das Buch.
Peter gives Mary the book.
b. ein Buch gibt Peter Maria.
c. dann gibt Peter Maria das Buch.
(2) a. Gibt Peter Maria das Buch?
b. Gib Maria das Buch!
(3) a. dass Peter Maria das Buch gi bt.
b. das Buch, das Peter Maria gibt.
Local Scrambling In the so-called “Mittelfeld”
all orders of arguments and adjuncts are poten-
tially possible. In the following example, all 5!
permutations are grammatical (Rambow, 1994):

(4) dass [eine Firma] [meinem Onkel] [die M¨obel] [vor
drei Tagen] [ohne Voranmeldung] zugestellt hat.
that [a company] [to my uncle] [the furniture] [three
days ago] [ without notice] delivered has.
Long-distance scrambling Objects of embed-
ded verbs can also be extraposed unboundedly
within the same sentence (Rambow, 1994):
(5) dass [den Schrank] [niemand] [zu reparieren] ver-
sprochen hat.
that [the wardrobe] [nobody] [to repair] promised
has.
3 A CCG for German
3.1 C ombinatory Categorial Grammar
CCG (Steedman (1996; 2000)) is a lexicalized
grammar formalism with a completely transparent
syntax-semantics interface. Since CCG is mildly
context-sensitive, it can capture the crossing de-
pendencies that arise in Dutch or German, yet is
efficiently parseable.
In categorial grammar, words are associ-
ated with syntactic categories, such as
or
for English intransitive and transitive
verbs. Categories of the form
or are func-
tors, which take an argument
to their left or right
(depending on the the direction of the slash) and
yield a result
. Every syntactic category is paired

with a semantic interpretation (usually a
-term).
Like all variants of categorial grammar , CCG
uses function application to combine constituents,
b ut it also uses a set of combinatory rules such as
composition (
) and type-raising ( ). Non-order-
preserving type-raising is used for topicalization:
Application:
Composition:
Type-raising:
Topicalization:
Hockenmaier a nd Steedman (2005) advocate
the use of additional “type-changing” rules to deal
with complex adjunct categories (e.g.
for ing-VPs that act as noun phrase mod-
ifiers). Here, we also use a small number of such
rules to deal with similar adjunct cases.
506
3.2 Capturing German word order
We follow Steedman (2000) in assuming that the
underlying word order in main clauses is always
verb-initial, and that the sententce-initial subject is
in fact topicalized. This enables us to capture dif-
ferent word orders with the same lexical category
(Figure 1). We use the features
and to
distinguish verbs in main and subordinate clauses.
Main clauses have the feature
, requiring ei-

ther a sentential modifier with category
,
a topicalized subject (
), or a
type-raised argument (
), where
can be any argument category, such as a noun
phrase, prepositional phrase, or a non-fi nite VP.
Here is the CCG derivation for the subordinate
clause (
)example:
dass Peter Maria das Buch gibt
For simplicity’s sake our extraction algorithm
ignores the issues that arise through local scram-
bling, and assumes that there are d ifferent lexical
category for each permutation.
1
Type-raising and composition are also used to
deal with wh-extraction and with long-distance
scrambling (Figure 2).
4 Translating Tiger graphs into CCG
4.1 The Tiger corpus
The Tiger corpus (Brants et al., 2002) is a pub-
licly available
2
corpus of ca. 50,000 sentences (al-
most 900,000 tokens) taken from the Frankfurter
Rundschau newspaper. The annotation is based
on a hybrid framework which contains features of
phrase-structure and dependency grammar. Each

sentence is represented as a graph whose nodes
are labeled with syntactic categories (NP, VP, S,
PP, etc.) and POS tags. Edges are directed and la-
beled with syntactic functions (e.g. head, subject,
accusative object, conjunct, appositive). The edge
labels are similar to the Penn Treebank function
tags, b ut provide richer and more explicit infor-
mation. Only 72.5% of the g raphs ha ve no cross-
ing edges; the remaining 27.5% are marked as dis-
1
Variants of CCG, s uch as Set-CCG (Hoffman, 1995) and
Multimodal-CCG (Baldridge, 2002), allow a more compact
lexicon for free word order languages.
2
/>continuous. 7.3% of the sentences hav e one or
more “secondary” edges, which are used to indi-
cate double dependencies that arise in coordinated
structures which are difficult to bracket, such as
right node raising, argument cluster coordination
or gapping. There are no traces or null elements to
indicate non-local dependencies or wh-movement.
Figure 2 sho ws the Tiger graph for a PP whose
NP argument is modified by a relative clause.
There is no NP level inside PPs (and no noun level
inside NPs). Punctuation marks are often attached
at the so-called “virtual” root (VR OOT) of the en-
tire graph. The relative pronoun is a dati ve object
(edge label DA) of the embedded infinitive, and
is therefore attached at the VP level. The relative
clause itself has the category S; the incoming edge

is labeled RC (relati ve clause).
4.2 T he translation algorithm
Our translation algorithm has the following steps:
translate(TigerGraph g):
TigerTree t = createTree(g);
preprocess(t);
if (t
null)
CCGderiv d = translateToCCG(t);
if (d
null);
if (isCCGderivation(d))
return d;
else fail;
else fail;
else fail;
1. Creating a planar tree: After an initial pre-
processing step which inserts punctuation that is
attached to the “virtual” root (VROOT) of the
graph in the appropriate locations, discontinuous
graphs are transformed into planar trees. Starting
at the lo west nonterminal nodes, this step turns
the Tiger graph into a planar tree without cross-
ing edges, where every node spans a contiguous
substring. This is required as input to the actual
translation s tep, since CCG derivations are pla-
nar binary trees. If the first to the
th child of a
node
span a contiguous substring that ends in

the
th word, and the th child spans a sub-
string starting at
, we attempt to move
the first
children of to its parent (if the
head position of
is greater than ). Punctuation
marks and adjuncts are s imply moved up the tree
and treated as if they were originally attached to
. This c hanges the syntactic scope of adjuncts,
b ut typically only VP modifi ers are affected which
could also be attached at a higher VP or S node
without a change in meaning. The main exception
507
1. The original Tiger graph:
an
in
APPR
einem
a
ART
Höchsten
Highest
NN
dem
whom
PRELS
sich
refl.

PRF
fraglos
without
questions
ADJD
habe
have
VAFIN
HD
HDMO
DA
SB OC
NKNK
AC RC
PP
VP
der
the
ART
Mensch
human
NN
kleine
small
ADJA
NK
NK
NK
NP
S

zu
to
PTKZU
unterwerfen
submit
VVVIN
PM HD
VZ
OA

,
$,
2. After transfo rmation into a planar tree and preprocessing:
PP
APPR-AC
an
NP-ARG
ART-HD
einem
NOUN-ARG
NN-NK
H¨ochsten
PKT
,
SBAR-RC
PRELS-EXTRA-DA
dem
S-ARG
NP-SB
ART-NK

der
NOUN-ARG
ADJA-NK
kleine
NN-HD
Mensch
VP-OC
PRF-ADJ
sich
ADJD-MO
fraglos
VZ-HD
PTKZU-PM
zu
VVINF
unterwerfen
VAFIN-HD
habe
3. The resulting CCG derivation
an
einem H¨ochsten
, dem
der
kleine Mensch
sich
fraglos
zu unterwerfen
habe
Figure 2: From T iger graphs to CCG derivations
are extraposed relative clauses, which CCG treats

as sentential modifiers with an anaphoric depen-
dency. Arguments that are moved u p are marked
as extracted, and an additional “extraction” edge
(explained below) from the original head is intro-
duced to capture the correct dependencies in the
CCG derivation. Discontinuous dependencies be-
tween resumptive pronouns (“place holders”, PH)
and their antecedents (“repeated elements”, RE)
are also dissolved.
2. Additional preprocessing: In order to obtain
the desired CCG analysis, a certain amount of pre-
processing is required. We insert NPs into PPs,
nouns into NPs
3
, and change sentences whose
first element is a complementizer (dass, ob,etc.)
into an SBAR (a cate gory which does not ex-
ist in the original Tiger annotation) with S argu-
3
The span o f nouns is gi ven by the NK edge label.
ment. This is necessary to obtain the desired CCG
derivations where complementizers and preposi-
tions tak e a sentential or nominal argument to their
right, whereas they appear at the same lev el as
their arguments in the Tiger corpus. Further pre-
processing is required to create the required struc-
tures for wh-extraction and certain coordination
phenomena (see below).
In figure 2, preprocessing of the o riginal Tiger
graph (top) yields the tree shown in the middle

(edge labels are shown as Penn Treebank-style
function tags).
4
We will first present the basic translation algo-
rithm before we explain how we obtain a deriva-
tion which captures the dependency between the
relative pronoun and the embedded verb.
4
We treat refle x ive pronouns as modifiers.
508
3. The basic translation step Our basic transla-
tion algorithm is very similar to Hockenmaier and
Steedman (2005). It requires a planar tree with-
out crossing edges, where each node is marked as
head, complement or adjunct. The latter informa-
tion is represented in the Tiger edge labels, and
only a small number of additional head rules is re-
quired. Each individual translation step operates
on local trees, which are typically flat.
N
C
C C C C
Assuming the CCG category of is , and its
head position is
, the algorithm traverses first the
left nodes
from left to right to create a
right-branching derivation tree, and then the right
nodes (
) from right to left to create a

left-branching tree. The algorithm starts at the root
category and recursively traverses the tree.
N
C
L
C L
R
R
R
H
C
C
The CCG category of complements and of the
root of the graph is determined from their Tiger
label. VPs are
, where the feature dis-
tinguishes bare infinitives, zu-infinitives, passives,
and (active) past participles. With the exception
of passives, these features can be determined from
the POS tags alone.
5
Embedded sentences (under
an SBAR-node) are always
. NPs and nouns
(
and ) have a case feature, e.g. .
6
Like
the English CCGbank, our grammar ignores num-
ber and person agreement.

Special cases: Wh-extraction and extraposition
In Tiger, wh-extraction is not explicitly marked.
Relativ e clauses, wh-questions and free relatives
are all annotated as S-nodes,and the wh-word is
a normal a rgument of the verb. After turning the
graph into a planar tree, we can identify these
constructions by searching for a relative pronoun
in the leftmost child of an S node (which may
be marked as extraposed in the case of e xtrac-
tion from an embedded verb). As sho wn in fig-
ure 2, we turn this S into an SBAR (a category
which does not exist in Tiger) with the first e dge
as complementizer and move the remaining chil-
5
Eventive (“werden”) passive is easily identified by con-
text; however, we found that not all stative ( “sein”) passives
seem to be annotated as such.
6
In some contexts, measure nouns (e.g. Mark, Kilometer)
lack case annotation.
dren under a new S node which becomes the sec-
ond daughter of the SBAR. The relative pronoun
is the head of this SBAR and tak es the S-node as
argument. Its category i s
, since all clauses
with a complementizer are verb-final. In order to
capture the long-range dependency, a “trace” is
introduced, and percolated down the tree, much
like in the algorithm of Hockenmaier and Steed-
man (2005), and similar to GPSG’s slash-passing

(Gazdar et al., 1985). These trace categories are
appended to the category of the head node (and
other arguments are type-raised as necessary). In
our case, the trace is also associated with the verb
whose argument it is. If the span o f this v erb
is within the span of a complement, the trace is
percolated down this complement. When the VP
that is headed by this verb is reached, we assume
a canonical order of arguments in order to “dis-
charge” the trace.
If a complement node is marked as extraposed,
it is also percolated down the head tree until the
constituent whose ar gument it is is found. When
another complement is found whose span includes
the span of t he constituent whose argument the ex-
traposed edge is, the extraposed category is perco-
lated down this tree (we assume extraction out of
adjuncts is impossible).
7
In order to capture the
topicalization analysis, main clause subjects also
introduce a trace. Fronted complements or sub-
jects, and the first adjunct in main clauses are ana-
lyzed as described in figure 1.
Special case: coordination – secondary edges
T iger uses “secondary edges” to represent the de-
pendencies that arise in coordinate constructions
such as gapping, argument cluster coordination
and right (or left) node raising (Figure 3). In right
(left) node raising, the shared elements are argu-

ments or adjuncts that appear on the right periph-
ery of the last, (or left periphery of the first) con-
junct. CCG uses type-raising and composition to
combine the incomplete conjuncts into one con-
stituent which combines with the shared element:
liest immer und beantwortet gerne jeden Brief.
always reads and gladly replies to every letter.
7
In our current implementation, each node cannot have
more than one forward and one backward extraposed element
and one forward and one backward trace. It may be preferable
to use list structures instead, especially for extraposition.
509
Complex coordinations: a Tiger graph with secondary edges
MO
während
while
KOUS
78
78
CARD
Prozent
percent
NN
und
and
KON
sich
refl.
PRF

aussprachen
argued
VVFIN
HD
SBCP
für
for
APPR
Bush
Bush
NE
S
OA

vier
vier
CARD
Prozent
percent
NN
für
for
APPR
Clinton
Clinton
NE
NK
AC
PP
NK

AC
PP
NK
NK
NP
NK
NK
NP
SB
MO
S
CDCJ CJ
CS
The planar tree after preprocessing:
SBAR
KOUS-HD
w¨ahrend
S-ARG
ARGCLUSTER
S-CJ
NP-SB
78 Prozent
PRF-MO
sich
PP-MO
f¨ur Bush
KON-CD
und
S-CJ
NP-SB

vier Prozent
PP-MO
f¨ur Clinton
VVFIN-HD
aussprachen
The r esulting CCG derivation:
w¨ahrend
78 Prozent
sich
f¨ur Bush
und
vier Prozent
f¨ur Clinton
aussprachen
Figure 3: Processing secondary edges in Tiger
In order to obtain this analysis, we lift such
shared peripheral constituents inside the conjuncts
of conjoined sentences CS (or verb phrases, CVP)
to new S (VP) level that we insert in between the
CS and its parent.
In argument cluster coordination (Figure 3), the
shared peripheral element (aussprachen)isthe
head.
8
In CCG, the remaining arguments and ad-
juncts combine via composition and typeraising
into a functor category which takes the cate gory of
the head as argument (e.g. a ditransitive verb), and
returns the same category that would result from
a non-coordinated structure (e.g. a VP). The re-

sult category of the furthest element in each con-
junct is equal to the category of the entire VP (or
sentence), and all other elements are type-raised
and composed with this to yield a category which
takes as ar gument a verb with the required subcat
frame and returns a verb phrase (sentence). Tiger
assumes i nstead that there are two conjuncts (one
of which is headless), and uses secondary edges
8
W¨ahrend has scope over the entire coordinated structure.
to indicate the dependencies between the head and
the elements i n the distant conjunct. Coordinated
sentences and VPs (CS and CVP) that have this
annotation are rebracketed to obtain the CCG con-
stituent structure, and the conjuncts are marked as
argument clusters. Since the edges in the argu-
ment cluster are labeled with their correct syntac-
tic functions, we are able to mimic the deriv ation
during category assignment.
In sentential gapping, the main v erb is shared
and appears in the middle of the first conjunct:
(6) Er trinkt Bier und sie Wein.
He drinks beer and she wine.
As in the English CCGbank, we ignore this con-
struction, which requires a non-combinatory “de-
composition” rule (Steedman, 1990).
5 Evaluation
Translation coverage The algorithm can fail at
several stages. If the graph cannot be turned into a
tree, it cannot be translated. This happens in 1.3%

(647) of all sentences. In many cases, this is due
510
to coordinated NPs or PPs where one or more con-
juncts are extraposed. We believe that these are
anaphoric, and further preprocessing could take
care of this. In other cases, this is due to verb top-
icalization (gegeben hat Peter Maria das Buch),
which our algorithm cannot currently deal with.
9
For 1.9% of the sentences, the algorithm cannot
obtain a correct CCG derivation. Mostly this is
the case because some traces and extraposed el-
ements cannot be discharged properly. Typically
this happens either in local scrambling, where an
object of the main verb a ppears between the aux-
iliary and the subject (hat das Buch Peter )
10
,or
when an argument of a noun that appears in a rel-
ative clause is extraposed to the right. There are
also a small number of constituents whose head is
not annotated. We ignore any gapping construc-
tion or argument cluster coordination that we can-
not get into the right shape (1.5%), 732 sentences).
There a re also a number of other constructions
that we do not currently deal with. We do not pro-
cess sentences if the root of the graph is a “virtual
root” that does not expand into a sentence (1.7%,
869). This is mostly the case for strings such as
Frankfurt (Reuters)), or if we cannot identify a

head child of the root node (1.3%, 648; mostly
fragments or elliptical constructions).
Overall, we obtain CCG derivations for 92.4%
(46,628) of all 54,0474 sentences, including
88.4% (12,122) of those whose Tiger graphs are
marked as discontinuous (13, 717), and 95.2%
of all 48,957 full sentences (excluding headless
roots, and fragments, but counting coordinate
structures such as gapping).
Lexicon size There are 2,506 lexical category
types, bu t 1,018 of these appear only once. 933
category types appear more than 5 times.
Lexical cov erage In order to evaluate coverage
of the extracted lexicon on unseen data, we split
the corpus into segments of 5,000 sentences (ig-
noring the last 474), and perform 10-fold cross-
v alidation, using 9 segments to extract a lexicon
and the 10th to test its coverage. Average cov er-
age is 86.7% (by token) of all lexical categories.
Coverage varies between 84.4% and 87.6%. On
average, 92% (90.3%-92.6%) of the lexical tokens
9
The corresponding CCG derivation combines the rem-
nant complements as in argument cluster coordination.
10
This problem arises because Tiger annotates subjects as
arguments of the auxiliary. We believe this problem could be
avoided if they w ere instead arguments of the non-finite verb.
that appear in the held-out data also appear in the
training data. On these seen tokens, coverage is

94.2% (93.5%-92.6%). More than half of all miss-
ing lexical entries are nouns.
In the English CCGbank, a lexicon extracted
from section 02-21 (930,000 tokens) has 94% cov-
erage on all tokens in section 00, and 97.7% cov-
erage on all seen tokens (Hockenmaier and Steed-
man, 2005). In the English data set, the proportion
of seen tokens (96.2%) is much higher, most likely
because of the relative lack of derivational and in-
flectional morphology. The better le xical coverage
on seen tokens is also to be expected, given that the
flexible word order of German requires case mark-
ings on all nouns as well as at least two different
categories for each tensed verb, and more in order
to account for local scrambling.
6 Conclusion and future work
We have presented an algorithm which converts
the syntax graphs in the German Tiger corpus
(Brants et al., 2002) into Combinatory Catego-
rial Grammar derivation trees. This algorithm is
currently able to translate 92.4% of all graphs in
T iger, or 95.2% of all full sentences. Lexicons
extracted from this corpus contain the correct en-
tries for 86.7% of all and 94.2% of all seen to-
kens. Good lexical coverage is essential for the
performance of statistical CCG parsers (Hocken-
maier and Steedman, 2002a). Since the Tiger cor-
pus contains complete morphological and lemma
information for all words, future work will address
the question of how to identify and apply a set of

(non-recursive) lexical rules (Carpenter, 1992) to
the extracted CCG lexicon to create a much larger
lexicon. The number of lexical category types is
almost twice as large as that of the English CCG-
bank. This is to be e xpected, since our gram-
mar includes case features, and German verbs re-
quire different categories for main and subordinate
clauses. We currently perform only the most es-
sential preprocessing steps, although there are a
number of constructions that might benefit from
additional changes (e.g. comparatives, parentheti-
cals, or fragments), both to increase coverage and
accuracy of the extracted grammar.
Since Tiger corpus is of comparable size to the
Penn Treebank, we hope that the work presented
here will stimulate research into statistical wide-
cov erage parsing of free word order languages
such as German with deep grammars like CCG.
511
Acknowledgments
I would like to thank Mark Steedman and Aravind
Joshi for many helpful discussions. This research
is supported b y NSF ITR grant 0205456.
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